The present invention relates to vessel segmentation in fluoroscopic images, and more particularly to learning-based hierarchical vessel segmentation in fluoroscopic images.
Coronary angiography is a minimally invasive medical procedure to restore blood flow through clogged coronary arteries. During this procedure, a catheter containing a guidewire is inserted through an artery in the thigh, and guided by a cardiologist through the arteries until it reaches the blocked coronary artery. The catheter is stopped immediately before the stenosis or blockage, and the guidewire is guided through the stenosis. A catheter with a deflated balloon is then inserted into the artery and guided along the guidewire until the balloon reaches the blockage. The balloon is then inflated and deflated repeatedly to unblock the artery, and a stent is placed at that position to prevent the artery from becoming blocked again.
The entire coronary angiography procedure is monitored with real-time fluoroscopic images. Fluoroscopic images are X-ray images taken over a period of time resulting in an image sequence. A contrast agent is injected into the artery in order to visualize the vessels (arteries) in the fluoroscopic images. This aids the cardiologist in the navigation of the catheter, guidewire, balloon, and stent in the vessels. During a coronary angiography procedure, the contrast agent is typically injected into the vessels several times. However, there are safety concerns involved with the repeated use of the contrast agent, such as an increase in the absorbed radiation rate in the tissue. Accordingly, it is desirable to reduce the amount of contrast agent used in the coronary angiography procedure.